EVALUATING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PREDICTIVE MAINTENANCE IN INDUSTRIAL MANUFACTURING SYSTEMS

Abstract

Artificial intelligence is now an indispensable part of modern industrial production and predictive maintenance is one of its uses. The study investigates the impact of AI-based predictive maintenance models on equipment performance, maintenance performance, and operation costs. The paper evaluates the accuracy of detection of problems, reduction of downtimes, maintenance savings, and system stability through the application of modern machine learning and deep learning methods to large volume of industrial sensor data. Its conclusion indicates that AI-based models are far more precise when forecasting when equipment will malfunction as compared to conventional maintenance approaches. The real life experience is that the maintenance planning has been significantly improved with reduced downtimes, improved utilization of resources and improved decision making capabilities. Besides, predictive maintenance with AI will assist equipment in achieving longer life and stabilize production. It is evident in the research that smart and data-driven maintenance plans enable taking action before things go wrong, reduce operational risks, and support long-lasting manufacturing operations. These data confirm the effectiveness of artificial intelligence as a key to the transformation of traditional methodologies of maintenance to intelligent, adaptive, and efficient solutions, which give considerable findings to the community of industry specialists and researchers.